Neural Network Web-Based Human Resource Management System Model (NNWBHRMSM)

International Journal of Computer Networks and Communications Security
VOL. 1, NO. 3, AUGUST 2013, 75–87
C
C
Available online at: www.ijcncs.org
N
ISSN 2308-9830
S
Neural Network Web-Based Human Resource Management
System Model (NNWBHRMSM)
Raphael Olufemi AKINYEDE1 and Oladunni Abosede DARAMOLA2
12Department of Computer Science,
The Federal University of Technology, P.M.B 704, Akure,
Ondo-State, Nigeria
E-mail: femi_akinyede@yahoo.com
ABSTRACT
As business activities are becoming increasing globally and as numerous firms expand their operations into
overseas markets, there is need for human resource management (HRM) to ensure that they hire and keep
good employees. From ages, firms/organizations have been having great problems in getting the right
professionals into appropriate jobs and training. This research focuses at exploiting information technology
in order to overcome these problems. The system, which is a network of inter–related processes, collects
data from applicants through a web-based interface and matches with appropriate jobs. This prevents the
frustration and some other problems inherent in the manual method of job recruitment, which is the
traditional unstructured interview and knowledge based method for matching applicants to jobs. The
proposed system is a neural network web-based human resource management system model running on
Internet Information (IIS) server with capabilities for Active Server Page (ASP) and Microsoft Access;
while Hypertext Markup Language (HTML) are used for authoring web pages. Finally, the system can run
on the minimum Pentium machines with Windows XP operating system.
Keywords: Human resource management, Knowledge base (KB), Inference engine (IE), Decision support
system (DSS), and Neural network.
1 OVERVIEW OF HUMAN RESOURCE
MANAGEMENT SYSTEM
According to Encarta (2012), it was reported that
businesses rely on effective human resource
management (HRM) to ensure that they hire and
keep good employees and that they are able to
respond to conflicts between workers and
management. HRM specialists initially determine
the number and type of employees that a business
will need over its first few years of operation. They
are then responsible for recruiting new employees
to replace those who leave and for filling newly
created positions. The understanding of human
resource management is important to anyone who
works in an organization; and wherever people
gather to work, personnel issues become important,
such issues like decision making concerning
recruitment, living, compensation, performance
evaluation, employee discipline, promotions and
transfer are of great and paramount importance.
The personnel in human resource management
department must understand all the rules and
regulations guiding the employees of the
firms/organizations; this is very important as it will
ensure that their everyday personnel actions are
consistent with those policies, to do otherwise is to
invite serious problems. As earlier noted, human
resource management is concerned with the
effective use of people in order to attain
organizational goals and enhance the personal
dignity, satisfaction, and well-being of employees.
But all these functions have been carried out
manually using traditional file system although few
organizations in Nigeria like Phillip Consulting
have gone computerized. For instance, the
conventional recruitment exercise involves a
process, which starts with a requisition from the
Head of each department of an organization who is
charged with the responsibility of evaluating,

76
R.O. Akinyede and O.A Daramola / International Journal of Computer Networks and Communications Security, 1 (3), August 2013
monitoring and controlling his departmental
budget. The requisition is passed onto the personnel
department, whose duty it is to schedule
appropriate recruitment, selection, placement and
training programs as shown in Figure 1 below.
Fig. 1. Human resources model
According to Akinyokun and Uzoka (1998),
personnel recruitment’s role has changed greatly
from one that has been based, largely, on the
traditional unstructured interview method to one
that is recognized as highly strategic and imperative
to the overall success of the organization. It was
added that, the role of the HR strategist is now
squarely focused on mechanisms to streamline the
human resource management (HRM) function in
order to contribute to the overall organization’s
success. Computer, which has remained one of the
most powerful tools, has served as an aid to
decision making in recent years, mostly because of
its efficiency in terms of speed, accuracy,
reliability, mass processing, cost and security,
among others. Thus, it is not uncommon to find
computers being applied in almost every human
activity. Presently, a new wave of awareness exists
in people as it concerns the use of computers in
administrative and qualitative information; it was
also confirmed that organizations adopted the use
of Management Information System (MIS) and
Decision Support Systems (DSS) in their decision
process and this has advanced to a web-based
human resource management system on the
platform of Internet. This research outlines the
benefits inherent in web-based human resource
management system to streamline processes,
outsource administrative activities, improve
efficiencies and reduce costs. With its user friendly
and technologically advance solution.
In addition, Akinyokun and Uzoka, (1998) also
reported that (HRM) involves the use of both
quantitative (structural) and qualitative
(unstructured) information. Decisions are largely
based on institution, principles and experience.
Now that the effort to build intelligence into
computing system, whereby the computer can be
used to process large volumes of quantitative and
qualitative information for decision making is
becoming reality.
On the whole, HR professionals continue to
perform many of the same activities that they did
decades ago e.g. training, recruiting, managing,
retaining and paying employees. The Internet,
however, has had a significant impact on the way
the HR professionals accomplish these tasks today,
where in the past, HR activities were largely paper-intensive
and highly manual, the function/process
today has been transformed into a sophisticated
computer-based process. Technological imp-rovements
have allowed HR professionals to spend
less time on administrative tasks and more time
with employees or employee candidates. It is
therefore, not uncommon today, to find some
organizations, most especially in developed
countries, employing the use of computing system
for their personnel recruitment and to an extent,
selection exercises. With such a system, the
applicant just feed his resumes into the computer
wherever he is, by responding to questions on the
screen by typing his/her answer, on the keyboard
and receives his employment information.
Straightaway, the resumes are fed into the organ-ization’s
central data bank, where they can be
quickly processed. HRM is an exciting and
dynamic field, even in this age of high information
technology; people are still the most important asset
to an organization.
Human resource is to support the organizations
mission, goals and strategies. The organizations
mission is to the purpose to which it is dedicated.
For example, the mission of an educational
institution is to create and disseminate knowledge.
The organizations goals and objectives state what it
wants to achieve. To accomplish the organizations
goals and support its strategies, human resources
objectives and strategies must also be developed.
A business’s HRM division also trains or
arranges for the training of its staff to encourage
Human
resources
needs
Recruitment
Selection
Placement
Training

77
worker productivity, efficiency, and satisfaction,
and to promote the overall success of the business.
Finally, human resource managers create workers’
compensation plans and benefit packages for
employees. The study developed a web-based
human resource management system, which will
allow applicants to visit the organization
employment website before they can enter their
data.
Globally, job vacancies are been advertised by
the personnel department and such advertisement
have the shortcomings which have become the
factors fueling the study of this research work, a
neural network web-based human resource
management system model (NNWBHRMSM).
Such shortcomings among others are:
i. Inability to get access to every potential
applicant due to the fact that the medium
chosen for the advertisement may not be such
that is accessible to them. The result of this is
that limited number of applicants that are
suitable for the jobs would only apply.
ii. Presently in many organizations, most
advertisements are only formalities, as
relatives of top managers fill the job positions
even before the advertisement are out.
iii. Due to high cost of advertisement, job
descriptions and specifications are not always
well defined to the effect that potential
applicants are misinformed about the
requirements, duties and remuneration
attached with the jobs.
iv. Applicants spend a lot of money producing
many copies of application letters and resumes
in response to advertisements, which would
have been filled online.
v. Many applications use to get lost in transit due
to poor performances of our postal services,
and in a situation where the selection process
is carried out, the applicants
The above stated shortcomings create a situation
whereby the organization fails to get right quality
and quantity of personnel to fill the available vacant
positions. Therefore, a web-based human resource
management system was proposed for an effective
recruitment process that would take care of these
shortcomings of the existing system. Such system
will have a data bank of employment opportunities
existing for different organizations and a
corresponding bank of potential applicants’
information obtained via the web. The main
objective of the research is to develop a
NNWBHRMSM, which will perform the
following:
i. enhancing the productivity of the human
resource personnel (HRP), thereby improving
the productivity of the corporate organization
they serve;
ii. reducing time wastages in collecting, sorting
and collating of applications from applicants;
iii. determining the potential of each employee in
order to ensure individual career growth and
personal dignity;
Since the system is client-server activities, it is
built on the World Wide Web (WWW) framework.
WWW provides a cost effective way of advertising
goods, services and vacancies. The research work
was carried out by an extensive review of related
literature. A thorough study of the current method
of recruiting applicants was carried out, and hence,
understanding the inadequacies. Afterwards many
recruiting organization were visited where personal
interviews with staff were conducted. The design of
the system was done using Hypertext Markup
Language (HTML) for authoring web pages and
Microsoft Access Database management system for
the design of the database tables. Actives server
pages (ASP) running on internet information server
was employed for the production and editing
HMTL pages. CorelDraw and Corel photo paint
were employed for the production and editing of
pictures and images. A browser, internet Explorer
was used at the client side to interpret contents got
from the web server; the browser processes the
HMTL and displays the web pages. The web
designs runs on Windows XP as the network
operating system. Real life data were used to test
the system so as to ensure that the design goals
were met.
The rest of the paper is structured as follows:-
Section 2 gave an overview of the related work in
the area of study. In sections 3 and 4, we presented
the proposed system model and framework for
web-based human resource management system
(WBHRMS) personnel procurement respectively.
However, sections 5 and 6 gave the system
implementation and security, while in section 7, we
concluded.

78
2 RELATED WORK
Getting the right professionals into appropriate
jobs and training has been the agitation of every
organization. For organization to do this, it must
understand the scope and policies involved. The
personnel department advertises job vacancies and
such advertisements are with some shortcomings.
The shortcomings created the situation whereby
organization fails to get right professionals into the
available vacancy. One of the attempts to solve the
shortcomings was made in Akintola, (1995). His
knowledge based application for matching
applicants to job could not be used for long
because, apart from being a Microsoft Disk
Operating System (MSDOS) based program, it was
a single user system that could not be used in a
networking environment. To improve on what
Akintola did, Uzoka, (1998) developed a
knowledge based system for matching applicants to
job. Apart from the fact that the system was an MS-DOS
based (single user) that could not been used in
a networking environment, it was not user friendly.
Users would need to learn lots of commands before
entering into the system. The system also attracts
high maintenance because of its relational structure.
In addition, it cannot be used in the present world
of computing because it could not be launched on
the Internet However, since users are
geographically distributed, we need a better system
and the best online human resources management
system program that took care of the shortcomings
was developed by Ogunwale, (2005). Ogunwale,
(2005) developed a web-based human resources
management system, which would have helped
organization in making decisions appropriately but
the system could not handled employment planning
and reports, wages and salaries, etc. Therefore, a
neural network web-based human resource
management system model which is an
improvement on the former ones was proposed.
The proposed system will have a data bank of
employment opportunities existing from different
organizations and corresponding bank of potential
applicants obtained through the Internet. The
system will help organizations in getting the right
professionals into appropriate jobs and training.
3 THE MODEL
We consider group of identical workers having
the same range of mass n. There is also a group of
identical organizations having the same type of
jobs, ck etc. Organizations use local formal methods
such as helpwanted sign posts, television adverts,
local or national newspapers. Workers who walk
throughout the city, listen to TV or radio adverts or
read local newspapers discover at random the
information about vacancies. This implies that
employed and unemployed workers have exactly
the same chance to hear about a vacancy. If the
worker is unemployed, he takes the job, ck. If he is
employed, we assume that he transmits this
information within his social network. Therefore,
unemployed workers can obtain a job either
indirectly through their employed friends (who
have heard about a vacant job) or directly. The
system can be mathematically model as follows:
Let c1, c2, …, ck be the jobs applicants applied
for; Un be the requirements for the job and Jm be the
qualification and experience of the potential
applicant sj, such that i, j, k = 1, 2, 3, ….., n.
Therefore, Sj, Un and Jm could be represented as
matrices as follows:
Sj ={S1, S2, S3, ……., Sk}, where {k =1,2,3, ….,j}
Un={U1, U2, U3, …., Ut}, where {t = 1, 2, 3, ….,n}
Jm ={J1, J2, J3, … …., Jx}, where {x = 1, 2, 3, ...,m}
The minimum requirements for the job ck, is a
row vector Ukt = [uk1 uk2 … ukn] and ukn  Un, t =
1, 2, 3, ….,n; and for each of the potential
candidate sj that applied for the job ck, let a = 1, 2,
3, …, p represent additional requirement from
which candidates are expected to be examined..
Also, the potential candidates sj’s job requirement
for the job ck is a row vector jip = [ji1 ji2 … jim]
and jim  Jm, p = 1, 2, 3, . …, m.
Here, we use a model to tune the coefficients for
the functions f1, f2, f3, f4 for the constraints and
evaluate their relative importance. The
corresponding conditional probability of the
occurrence of the job to be offered is
l  P(decision  1| w)  g(wL f ) (i)
a

f a e a
( ) ii
( )
1
e

Where g represents the logistic function
evaluated at activation a. Let w denote weight
vector and f the column vector of the importance
functions: [ ,..., ] 1 5 f L  f f . Then the “decision”
is generated according to the model.
The weight vector w can be adapted using feed
forward neural network (FFNN) topology
(Schumacher, et al. 1996) and (Biganzoli, et al.
1998). In the simplest case there is one input layer

79
and one output logistic layer. This is equivalent to
the generalized model with logistic function. The
estimated weights satisfy Eq.(iii):
 w  1, 0  w 
1 (iii)
i i i
The linear combination of weights with inputs
f1,...,f4 is a monotone function of conditional
probability, as shown in Eq.(i) and Eq.(ii), so the
conditional probability of job, ck to be offered can
be monitored through the changing of the
combination of weights with inputs f1, f2, f3, f4. The
classification of decision can be achieved through
the best threshold with the largest estimated
conditional probability from group data. The class
prediction of an observation x from group y was
determined by
C(x) argmax Pr(x | l k) (iv) k  
To find the best threshold we used Receiver
Operating Characteristic (ROC) to provide the
percentage of detections correctly classified and the
non-detections incorrectly classified. To do so we
employed different thresholds with range in [0,1].
To improve the generalization performance and
achieve the best classification, the Multilayer
Perceptron with structural learning was employed
(Kozma, et al. 1996) and (Ishikawa, 1996).
Such as for each ck and each sj has applied for:
v iff k sj   w 2
Job Vacancy:- { }, 1 4 1 f  c  k  k
( ) ( ), 1 1 F f V f
Where F is the mapping function between
personal data and job vacancy.
c p iff c is true k k , k 

 k
 k v k k
iff c w where T
int
,
,
w
T
is the total po
Personal data: { }, 1 4 2 f  s  j  j
( ) ( ), 2 2 F f V f
s p iff s is true j j, j 

 j
 j v j j
iff s w where T
int
,
,
w
T
is the total po
Academic qualification: { }, 1 4 3 f  j  jm m
( ) ( ), 3 3 F f V f
j p iff j is true m m, m 

 m
 jm v m m
iff j p where T
int
,
,
p
T
is the total po
Job History: { }, 1 4 4 f  u  n  n
( ) ( ), 4 4 F f V f
u p iff u is true n n, n 

 n
 n v n n
iff u p where T
int
,
,
p
T
is the total po
Note that sj, ck, jm and un are inputs; and pn, pk, pm,
pn =po are points, yn, yk, ym and yn = yi are bias, vi =
weighs, oi = outputs.
H represent the function that maps Un and Jm.
Then we have
H(J Matches m) (Un)
Let t equals total jobs applied for. If Jm ≠ Un and
not end of file (i.e. the applicant qualification does
not meet the job requirement), then process sj = .
Otherwise Xr = M(Jm), r = 1, 2, 3 ….., n, where M
is a function that returns the list of short listed
applicants st .
Therefore, obtain Xr = M(Jm).
If  (t) p
t
r

 
1
, select next  otherwise access the
next candidate.
The final shortlisted is expressed as follows

80

  


f shortlisted 
0, 1
n
r
r X p
provided
otherwise
1,
n
where
r
r X
1
represents the total candidates st that
are qualified for the job ck and the f_shorlistedi
represents the shortlisted candidates and p represent
the maximum requirement for the job k, where Jm 
Un
The description and architecture of the artificial
network are hereby given.
One natural way the decision making problem
can be addressed is via the tuning the coefficients
for the soft constraints. This will largely simplify
the architecture, and it saves on both running time
and memory. Decision making can also be viewed
as a classification problem, for which neural
networks demonstrated to be a very suitable tool.
Neural networks can learn to make human-like
decisions, and would naturally follow any changes
in the data set as the environment changes,
eliminating the task of re-tuning the coefficients.
Figure 2 above shows the architecture of the feed
forward neural network of a web-based human
resource management system (WBHRMS). The
neural network architectures have three (3) layers.
The first layer, which is the only layer exposed to
external signals is called the input layer. The layer
accepts signal (such as applicant’s resume) and
transmits it to the neurons in the next layer, which
is the hidden layer. Each of these layers is linked to
several other hidden layers between the input and
output layers of the network. The layer, which may
be several layers of hidden nodes, performs a
calculation on the signals reaching it and sends a
corresponding output signal to other layers. The
layer will extracts relevant features or patterns
(employees’ job specifications) from the received
signals. The final outputs are highly processed
version of the input, which are then directed to the
output layer -the final layer of the network.
Fig. 2. Artificial neural network model of the WBHRMS
A simplified model of a neuron can easily be
simulated by an artificial neuron shown in figure 3.
The variables sj, ck, jm and un which represent the
input line at a particular point in time has one
output line each representing the axon of the
neuron.
Fig. 3. Final selection for Neural Network Model of
the WBHRMS

81
The input-output behaviour of the artificial
neuron is now by
Test:
{ }, 1 3 , , , f  s c j u  n  n n k m n
( n ) ( n ), F f V f
where F is the mapping function between
p iff s j u are true n n n o, n, n, n s , j , u 

 o
 n v n n n o
iff s , j u p ,
where
int
,
,
p
T
T is the total po
The variable po is called the weight of input line i
and represent the synaptic transmission efficiency
of the synapse between the final filament of a
neuron and the dendrites i of a particular neuron.
4 FRAMEWORK FOR WEB-BASED
HUMAN RESOURCE MANAGEMENT
SYSTEM (WBHRMS) PERSONNEL
PROCUREMENT THE MODEL
The framework proposed for web-based
personnel procurement is conceptualized in figure
5 and 6. In this section, we present the relational
form of the human resources management
conceptual objects. Statistical procedure is used for
analyzing the operation and implementation of
efficient human resources management system.
The system design is aimed at effective and
efficient human resources management on the
Internet. The global chart of the database design is
as shown in fig. 4.
The major components of the framework are the
following, namely: knowledge base, database,
inference engine, decision support system
Fig. 4. Global chart of the database design
The knowledge base provides specific domain
knowledge (facts and rules) about the subject
acquired from the domain experts. It is designed
based on rules, which combined quantitative
(structured) and qualitative (unstructured)
knowledge/information and it serves as the
information store for the operational data that are to
be processed. It contains information about the
prospective job applicant and the job requirements
as are sent by the establishments employing the
services of job bureau. The knowledge base of
WBHRMS contains two major inter-related
databases, namely: job requirement database,
applicant database and other databases (Ifeta,
2006). The illustrative architecture of the proposed
knowledge based system for job procurement is
conceptualized in figure 3.
In the database, the entire knowledge base can be
conceptualized as a network of relations. A relation
is a two-dimensional table that has a number of
rows and columns. It is synonymous with the ‘file’
concept in the conventional data processing
environment. The database objects are
conceptualized using a relational database model. A
relation is similar to what is customarily referred to
as a file and it is generally represented by a set of
structured tuples. Each tuple of a relation
corresponds to a record in a file and attributes
correspond to fields within a record. The general
form of a relation is given by
R [A1, A2, A3…... AK, AK+1……….An],
where R represents the name of the relation, and
the set {Ai}, i = 1,2,3…,n, represents attributes of
the relation R (Codd, 1970). A role-based
mechanism is built into the system to specify access
rights to the database system.
The web-based human resource management
system has six relations in its knowledge base. The
first five relations contain structured information,
while the last relation contains unstructured
information modeled in a relation using indicator of
performance. The relational database supported by
the system includes:

82
Fig. 5. Illustrative Architecture of the Proposed Knowledge Based System
i. APPLICANT-PERSONAL-DATA
[applicant no, surname, other names, date-of-
birth, sex, nationality, state-of-origin,
marital-status, next-of-kin, age, address,
email,
ii. APPLICANT-ACADEMIC/
PROFESSIONAL-QUALIFICATION
[applicant no, date of
award, certificate, place-of-award, major
subject, minor subject, class-of-award,
name, pdate of award, pstatus].
iii. APPLICANT-JOB-HISTORY [applicant
no, date employed, date disengaged, job
code, status, employer, last-salary,
condition for leaving, name, promotion,
development, leaves, medical, min year].
iv. ORGANIZATION [organization no,
address, telephone-no, line-of-trade].
v. JOB REQUIREMENT [job code, degree,
status, sex, age, nationality, years of
experience, job status of the organization].
vi. JOB VACANCY [organization no, job
code, job title, vacancy, email].
vii. PERFORMANCE [applicant no, job-code,
physical-test, intelligence-test, aptitude-test,
score].
The inference engine (IE) provides the reasoning
ability that enables the expert system to form
conclusions from specific facts and rules about the
subject provided by the knowledge base. The
applications server would receive request/resumes
from different applicants and sends it to them to the
corporate server for heavy processing tasks. This
module does the actual searching for and matching
of applicant’s information/qualification against the
job request. WBHRMS adopts backward chaining
method of making inferences. The proposed system
looks at a particular job request and then search for
the set of applicants that meet the requests and
score them accordingly. The results are later sent
to the qualified applicants through their email
addresses.
The knowledge about applicant is composed of
the following:
i. Personal data.
ii. Academic and professional qualifications.
iii. Job history,
While the knowledge about the job is composed
of the following:
i. Applicants’ registration.
ii. Job and organization requirements.
iii. Job vacancy
In each of the phases below, the inferences drawn
will lead to the matching of another phase. See
figure 6 below.

83
Phase I: The system studies applicants and jobs
Phase II: Matching of applicants to job
requirement.
Phase III: Matching of Academic/Professional
qualifications with job requirement.
Figure. 6. Illustrate Architecture for the Matching
System
The inference engine (IE) provides the reasoning
ability that enables the expert system to form
conclusions from specific facts and rules about the
subject provided by the knowledge base with the
corresponding decision variables of the job
requirements knowledge and report a list of
applicants that are selected for some specific jobs.
Decision Support System (DSS) has two sub-systems,
namely cognitive and emotional filters.
The cognitive filter carries out series of
reasoning, which include the inductive and
deductive reasoning, on the information contents of
the list of applicants appointable for a given job
produced by the inference engine. For example,
some steps could be taking in making decisions
concerning the most suitable qualification and
additional qualifications for a particular job, age
limits for the job, working experiences in the areas
related to the job, locations of the applicants, sex –
whether male or female, stature, status –whether
married or not married, etc. All these could form
the basis for cognitive filtering of the list of
selected applicants as programmed by the system
engineer.
The emotional filter carries out series of
reasoning, which also include the inductive and
deductive reasoning, on the information context of
the list of applicants appointable for a given job
produced by the inference engine. For example, a
candidate could be preferred because of his
relationship with the people in the authority;
candidate could be disqualified because of bad
behaviour, a male candidate could be preferred to
his female counterpart because of the stress that is
going to be involved, candidate might be
disqualified on health ground, another one on tribe,
etc. All these could form the basis for emotional
filtering of the list of selected applicants as
programmed by the system engineer.
The proposed system supports a user interface
based on the interactive web browser known as
internet explorer and access is gained by supplying
username and password both of which aid the
control of access to the website. The selection of
each main menu leads to other sub-menus, which
calls on inference procedure associated with that
menu. The inference procedure is interactive and it
guides intelligently to supply appropriate
information. On selection of any of the menus,
alternative matching decisions and reasoning
behind the decisions will be presented to the
expert. Finally, the system administrators will have
the choice of applicants to match and
recommendations will be made to human resources
department of the firm/organization concerned. See
the figure below.
Matching
process
Knowledge about
applicants
Knowledge about
jobs
Applicants Matching
process
Job requirements
Academic/
Professional
qualifications
Job
requirements
Matching
process

84
Fig. 7. Data Flow Diagram
5 THE SYSTEM IMPLEMENTATION
The use of Internet has moved from the old static
view and download of information to more
sophisticated dynamic use, such as e-commerce, e-government
and e-business. Any functioning site
contains clients connected to server via network
resources. The clients contain the browser, which
display any information downloaded from the
server. In addition, through the clients,
information/data are uploaded to server for
appropriate processing. In this regard, a website
that could assist any organization to receive its
applicant’s data via internet is being developed.
With this website, the applicants can search for
organizations with vacancies and their addresses.
Having gotten any appropriate firm of interest,
employment forms are made available for them to
fill and submit, which are in turn uploaded to the
organizations’ server computers. Through an
application developed in Internet Information
Server, individual firm can then get connected to
their server computers and retrieve the applicant
data for processing.
Due to large flexibility of information delivery
over the Internet, the system is implemented as a
standard web-based application. The applicant side
requires no more than standard Internet browser
installed on the local machine which the main
application functionality is assured by the server
side. The basic component of the system
infrastructure is presented in Figure 8.

85
Fig. 8. Conceptual Diagram of the WBHRMS
The diagram in figure 8 is a three-layer Internet
architecture which consists of presentation, content
management system and data services. This
architecture allows user to access the system
through the Internet HTTP protocol and the user’s
request is transformed into a structure query
language using an Active Server Page common
content management gateway, which in turn passes
it to the appropriate backend system. The common
content management gateway provides a single
point entry to the system via a URL. From the
figure below, presentation consists of two main
parts. The first part is the user interface to the
system. User interface is based on HTML; so only
browser such as internet explorer is required to use
the system at the client (applicant) side. The
second part is the Internet Information Server (IIS)
web server. The content management system
represents the interface between the presentation
and data services. At the content management
services, user’s request is transformed into a
structured query language where need be using
ASP scripts. Data services represent database
management system, and Microsoft Access is used
to provide the required functionality. The system
dynamically creates and returns an HTML page
with the results of operation specified by the user
to the browser.
Fig. 9. The System Conceptual Architecture
Figure 7 and 9 show the data flow diagram,
which includes one static HTML for home page
(with forms for login to the system), and two CGI
programs for performing authentication, and
database accessing definitions. This diagram also
serves as the web delivery design diagram.
6 SECURITY
The proposed system must be carefully secured
from abuse and facilities must be put in place to
ensure the security of the system against
unauthorized use. In order to ensure that
unauthorized transactions are not entered

86
undetected the system will not allow any
transaction or enquiry unless a user (company user)
has logged on and entered the correct username and
a password. Access will be denied to unauthorized
users, who will be logged out after a predetermined
number of trails. Each structure or staff is assigned
a unique username and password, which is store in
the administration part of the database. This
prevents unauthorized access to the system. After a
successful authentication, the main menu of the
system will be loaded. Each applicant using the site
will register and after registration username and
password will be communicated through the email
addressed submitted by such applicant. In addition,
physical access to the computer system, diskette
and auxiliary items should be restricted to
authorized staff. There are adequate programmed
controls on the facilities for system maintenance.
For example where some data are becoming
obsolete and unnecessarily occupying space on the
system, provision is been made to remove such
data. This will however only be done by authorized
user.
7 CONCLUSION
The system would be of great importance in
assisting human experts in solving problems
associated to job procurement. It has definitely
replaced the traditionally manual components of
background investigation by providing an
automated data retrieval process in order to make
effective and timely decisions. The knowledge
engineer uses the knowledge obtained from human
experts to design the system package and draw
inferences based on some rules concerning the
static and dynamic data contained in the data bank.
This would enables the main objective of the site
which is to build a web-based system that will
assist the human resources department in procuring
staff without necessarily going through the rigours
and problems associated with the conventional
manual method of procuring staff, to be achieved.
The research developed a neural network web-based
human resource management system model
(NNWBHRMSM) that has solved the problems
associated with the past researchers especially, the
one pointed out in Ogunwale, (2005), that is,
inability to handle employment planning, reports,
salaries and wages. Finally, the system
NNWBHRMSM, which addresses performance,
based on aptitude and intelligence tests is a
promising one.
8 REFERENCES
[1] AKINTOLA, K. G., 1995, Knowledge Based
Application System for Matching Applicants to
Job: B. Tech. Thesis. The Federal University of
Technology, Akure, Ondo State, Nigeria.
[2] AKINYOKUN, O. C and UZOKA F.M.E,
1998, A prototype on Information Technology
Based Human Resource System,
http://www.journal.au.edu/mcim/jan00/uzoka.d
oc
[3] AKINYOKUN, O.C, 2000, Computer: A
Partner to human experts 23rd Inaugural
lecture of The Federal University of
Technology Akure, Nigeria.
[4] BIGANZOLI, E., BORACCHI, P., MARIANI,
L. and MARUBINI, E., 1998, Feed Forward
Neural Networks for the Analysis of Censored
Survival Data: A Partial Logistic Regression
Approach”, Statistics in Medicine, 17, pp.
1169-1186.
[5] CODD, E., 1970, Relational Model for Large
Shared Data Banks, Communication of ACM,
Vol. 13, No. 6., pp377-387.
[6] ENCARTA, 2012, Microsoft Corporation. All
rights reserved.
[7] IFETA, U. C., 2006, Design and
implementation of a web based human
resource management system (WBHRMS),
H.N.D Project Report, Mathematics, Statistics
and Computer Science Department, the Federal
Polytechnic, Ado- Ekiti, Ekiti State.
[8] ISHIKAWA, M., 1996, Structural learning
with forgetting, Neural Networks, Vol. 9, pp.
509-521.
[9] KOZMA, R., SAKUMA, M., YOKOYAMA,
Y. and KITAMURA, M., 1996, On the
Accuracy of Mapping by Neural Networks
Trained by Back porpagation with Forgetting.,
Neurocomputing, Vol. 13, No. 2-4, pp. 295-
311.
[10] OGUNWALE, Y. E., 2005, Development of a
web-based human resource management
system: M. Tech. Thesis. The Federal
University of Technology, Akure, Ondo State,
Nigeria.
[11] SCHUMACHER, M., ROSSNER, R. and
VACH, W., 1996, Neural networks and logistic
regression: Part I, Computational Statistics and
Data Analysis, 21, pp. 661-682.

87
[12] STUART, J. RUSEEL and PETER NORVIG,
Artificial Intelligence A modern approach,
Prentice hall, Upper saddle River, New Jersey
07458 Pages 4,5
[13] UZOKA, F. M. E., 1998, Knowledge Based
System for Matching Applicants to Job: M.
Tech. Thesis. The Federal University of
Technology, Akure, Ondo State, Nigeria.

Neural Network Web-Based Human Resource Management System Model (NNWBHRMSM)

Recommended

Recommended

More Related Content

What's hot

What's hot (18)

Viewers also liked

Viewers also liked (20)

Similar to Neural Network Web-Based Human Resource Management System Model (NNWBHRMSM)

Similar to Neural Network Web-Based Human Resource Management System Model (NNWBHRMSM) (20)

Recently uploaded

Recently uploaded (20)

Neural Network Web-Based Human Resource Management System Model (NNWBHRMSM)